import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
   import torch

if is_datasets_available():
   from datasets import load_dataset

lowerCamelCase__  =					random.Random()


def 				lowerCAmelCase__       (			a__    ,     a__=1.0    ,     a__=None    ,     a__=None )      ->Optional[Any]:




  '''simple docstring'''

  if rng is None:
    _UpperCamelCase           =    global_rng

  _UpperCamelCase           =    []
  for batch_idx in range(shape[0] ):
    values.append([] )
    for _ in range(shape[1] ):
      values[-1].append(rng.random() * scale )

  return values




class 					_UpperCAmelCase			(     unittest.TestCase  ):



 '''simple docstring'''




 def __init__(		self				:   int							, lowercase_				:   Dict							, lowercase_				:   Optional[int]=7							, lowercase_				:   List[str]=400							, lowercase_				:   Dict=2000							, lowercase_				:   List[Any]=2048							, lowercase_				:   Union[str, Any]=128							, lowercase_				:   Any=1							, lowercase_				:   str=512							, lowercase_				:   int=30							, lowercase_				:   Tuple=44100							, )						->	Optional[int]:


   """simple docstring"""







   _UpperCamelCase           =    parent
   _UpperCamelCase           =    batch_size
   _UpperCamelCase           =    min_seq_length
   _UpperCamelCase           =    max_seq_length
   _UpperCamelCase           =    (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
   _UpperCamelCase           =    spectrogram_length
   _UpperCamelCase           =    feature_size
   _UpperCamelCase           =    num_audio_channels
   _UpperCamelCase           =    hop_length
   _UpperCamelCase           =    chunk_length
   _UpperCamelCase           =    sampling_rate







 def 		__UpperCAmelCase				(		self				:   Union[str, Any])						->	int:


   """simple docstring"""







   return {
       "spectrogram_length": self.spectrogram_length,
       "feature_size": self.feature_size,
       "num_audio_channels": self.num_audio_channels,
       "hop_length": self.hop_length,
       "chunk_length": self.chunk_length,
       "sampling_rate": self.sampling_rate,
   }







 def 		__UpperCAmelCase				(		self				:   Dict							, lowercase_				:   str=False							, lowercase_				:   Dict=False)						->	Optional[int]:


   """simple docstring"""







   def _flatten(lowercase_				:   Union[str, Any]):
     return list(itertools.chain(*lowercase_))

   if equal_length:
     _UpperCamelCase           =    [floats_list((self.max_seq_length, self.feature_size)) for _ in range(self.batch_size)]
   else:
     # make sure that inputs increase in size
     _UpperCamelCase           =    [
         floats_list((x, self.feature_size))
         for x in range(self.min_seq_length							, self.max_seq_length							, self.seq_length_diff)
     ]
   if numpify:
     _UpperCamelCase           =    [np.asarray(lowercase_) for x in speech_inputs]
   return speech_inputs




@require_torch
@require_torchaudio
class 					_UpperCAmelCase			(     lowerCAmelCase,			unittest.TestCase  ):



 '''simple docstring'''




 __A			       =					TvltFeatureExtractor







 def 		__UpperCAmelCase				(		self				:   Union[str, Any])						->	Tuple:


   """simple docstring"""







   _UpperCamelCase           =    TvltFeatureExtractionTester(self)







 def 		__UpperCAmelCase				(		self				:   List[Any])						->	List[str]:


   """simple docstring"""







   _UpperCamelCase           =    self.feature_extraction_class(**self.feat_extract_dict)
   self.assertTrue(hasattr(lowercase_							, "spectrogram_length"))
   self.assertTrue(hasattr(lowercase_							, "feature_size"))
   self.assertTrue(hasattr(lowercase_							, "num_audio_channels"))
   self.assertTrue(hasattr(lowercase_							, "hop_length"))
   self.assertTrue(hasattr(lowercase_							, "chunk_length"))
   self.assertTrue(hasattr(lowercase_							, "sampling_rate"))







 def 		__UpperCAmelCase				(		self				:   List[str])						->	Union[str, Any]:


   """simple docstring"""







   _UpperCamelCase           =    self.feature_extraction_class(**self.feat_extract_dict)

   with tempfile.TemporaryDirectory() as tmpdirname:
     _UpperCamelCase           =    feat_extract_first.save_pretrained(lowercase_)[0]
     check_json_file_has_correct_format(lowercase_)
     _UpperCamelCase           =    self.feature_extraction_class.from_pretrained(lowercase_)

   _UpperCamelCase           =    feat_extract_first.to_dict()
   _UpperCamelCase           =    feat_extract_second.to_dict()
   _UpperCamelCase           =    dict_first.pop("mel_filters")
   _UpperCamelCase           =    dict_second.pop("mel_filters")
   self.assertTrue(np.allclose(lowercase_							, lowercase_))
   self.assertEqual(lowercase_							, lowercase_)







 def 		__UpperCAmelCase				(		self				:   str)						->	Dict:


   """simple docstring"""







   _UpperCamelCase           =    self.feature_extraction_class(**self.feat_extract_dict)

   with tempfile.TemporaryDirectory() as tmpdirname:
     _UpperCamelCase           =    os.path.join(lowercase_							, "feat_extract.json")
     feat_extract_first.to_json_file(lowercase_)
     _UpperCamelCase           =    self.feature_extraction_class.from_json_file(lowercase_)

   _UpperCamelCase           =    feat_extract_first.to_dict()
   _UpperCamelCase           =    feat_extract_second.to_dict()
   _UpperCamelCase           =    dict_first.pop("mel_filters")
   _UpperCamelCase           =    dict_second.pop("mel_filters")
   self.assertTrue(np.allclose(lowercase_							, lowercase_))
   self.assertEqual(lowercase_							, lowercase_)







 def 		__UpperCAmelCase				(		self				:   int)						->	Any:


   """simple docstring"""







   _UpperCamelCase           =    self.feature_extraction_class(**self.feat_extract_dict)

   # create three inputs of length 800, 1000, and 1200
   _UpperCamelCase           =    [floats_list((1, x))[0] for x in range(800							, 1400							, 200)]
   _UpperCamelCase           =    [np.asarray(lowercase_) for speech_input in speech_inputs]

   # Test not batched input
   _UpperCamelCase           =    feature_extractor(np_speech_inputs[0]							, return_tensors="np"							, sampling_rate=44100).audio_values

   self.assertTrue(encoded_audios.ndim == 4)
   self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size)
   self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length)
   self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels)

   # Test batched
   _UpperCamelCase           =    feature_extractor(lowercase_							, return_tensors="np"							, sampling_rate=44100).audio_values

   self.assertTrue(encoded_audios.ndim == 4)
   self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size)
   self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length)
   self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels)

   # Test audio masking
   _UpperCamelCase           =    feature_extractor(
       lowercase_							, return_tensors="np"							, sampling_rate=44100							, mask_audio=lowercase_).audio_values

   self.assertTrue(encoded_audios.ndim == 4)
   self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size)
   self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length)
   self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels)

   # Test 2-D numpy arrays are batched.
   _UpperCamelCase           =    [floats_list((1, x))[0] for x in (800, 800, 800)]
   _UpperCamelCase           =    np.asarray(lowercase_)
   _UpperCamelCase           =    feature_extractor(lowercase_							, return_tensors="np"							, sampling_rate=44100).audio_values
   self.assertTrue(encoded_audios.ndim == 4)
   self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size)
   self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length)
   self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels)







 def 		__UpperCAmelCase				(		self				:   Optional[int]							, lowercase_				:   List[Any])						->	Optional[Any]:


   """simple docstring"""







   _UpperCamelCase           =    load_dataset("hf-internal-testing/librispeech_asr_dummy"							, "clean"							, split="validation")
   # automatic decoding with librispeech
   _UpperCamelCase           =    ds.sort("id").select(range(lowercase_))[:num_samples]["audio"]

   return [x["array"] for x in speech_samples]







 def 		__UpperCAmelCase				(		self				:   Optional[Any])						->	List[str]:


   """simple docstring"""







   _UpperCamelCase           =    self._load_datasamples(1)
   _UpperCamelCase           =    TvltFeatureExtractor()
   _UpperCamelCase           =    feature_extractor(lowercase_							, return_tensors="pt").audio_values

   self.assertEquals(audio_values.shape							, (1, 1, 192, 128))

   _UpperCamelCase           =    torch.tensor([[-0.30_32, -0.27_08], [-0.44_34, -0.40_07]])
   self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]							, lowercase_							, atol=1e-4))





from __future__ import annotations


def 				lowerCAmelCase__       (			a__    ,     a__ )      ->bool:




  '''simple docstring'''

  _UpperCamelCase           =    get_failure_array(a__ )

  # 2) Step through text searching for pattern
  _UpperCamelCase      ,						_UpperCamelCase           =    0, 0  # index into text, pattern
  while i < len(a__ ):
    if pattern[j] == text[i]:
      if j == (len(a__ ) - 1):
        return True
      j += 1

    # if this is a prefix in our pattern
    # just go back far enough to continue
    elif j > 0:
      _UpperCamelCase           =    failure[j - 1]
      continue
    i += 1
  return False


def 				lowerCAmelCase__       (			a__ )      ->list[int]:




  '''simple docstring'''

  _UpperCamelCase           =    [0]
  _UpperCamelCase           =    0
  _UpperCamelCase           =    1
  while j < len(a__ ):
    if pattern[i] == pattern[j]:
      i += 1
    elif i > 0:
      _UpperCamelCase           =    failure[i - 1]
      continue
    j += 1
    failure.append(a__ )
  return failure


if __name__ == "__main__":
   # Test 1)
   lowerCamelCase__  =					'''abc1abc12'''
   lowerCamelCase__  =					'''alskfjaldsabc1abc1abc12k23adsfabcabc'''
   lowerCamelCase__  =					'''alskfjaldsk23adsfabcabc'''
   assert kmp(pattern, texta) and not kmp(pattern, texta)

   # Test 2)
   lowerCamelCase__  =					'''ABABX'''
   lowerCamelCase__  =					'''ABABZABABYABABX'''
   assert kmp(pattern, text)

   # Test 3)
   lowerCamelCase__  =					'''AAAB'''
   lowerCamelCase__  =					'''ABAAAAAB'''
   assert kmp(pattern, text)

   # Test 4)
   lowerCamelCase__  =					'''abcdabcy'''
   lowerCamelCase__  =					'''abcxabcdabxabcdabcdabcy'''
   assert kmp(pattern, text)

   # Test 5)
   lowerCamelCase__  =					'''aabaabaaa'''
   assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]




